Correction to: Factors associated with death in confirmed cases of COVID-19 in the state of Rio de Janeiro

Oliveira, Marcella Cini; Eleutério, Tatiana de Araújo; Corrêa, Allan Bruno de Andrade; Silva, Lucas Dalsenter Romano da; Rodrigues, Renata Coelho; Oliveira, Bruna Andrade de; Martins, Marlos Melo; Raymundo, Carlos Eduardo; Medronho, Roberto de Andrade

doi:10.1186/s12879-021-06410-2

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…Cases that are younger or without any preexisting conditions or breathing difficulties had the best prognosis. Also, consistent with the literature, cases in our cohort who had breathing difficulties irrespective of age or presence of preexisting conditions had higher rates of hospitalization ( 7 , 29 ). Modeling studies based on different cohorts have shown age is a key factor associated with poor outcomes.…”

Section: Discussionsupporting

confidence: 91%

Predicting the Risk Factors Associated With Severe Outcomes Among COVID-19 Patients–Decision Tree Modeling Approach

Kumaran

Pham

Wang

et al. 2022

Front. Public Health

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BackgroundThe COVID-19 pandemic has seen a large surge in case numbers over several waves, and has critically strained the health care system, with a significant number of cases requiring hospitalization and ICU admission. This study used a decision tree modeling approach to identify the most important predictors of severe outcomes among COVID-19 patients.MethodsWe identified a retrospective population-based cohort (n = 140,182) of adults who tested positive for COVID-19 between 5th March 2020 and 31st May 2021. Demographic information, symptoms and co-morbidities were extracted from a communicable disease and outbreak management information system and electronic medical records. Decision tree modeling involving conditional inference tree and random forest models were used to analyze and identify the key factors(s) associated with severe outcomes (hospitalization, ICU admission and death) following COVID-19 infection.ResultsIn the study cohort, nearly 6.37% were hospitalized, 1.39% were admitted to ICU and 1.57% died due to COVID-19. Older age (>71Y) and breathing difficulties were the top two factors associated with a poor prognosis, predicting about 50% of severe outcomes in both models. Neurological conditions, diabetes, cardiovascular disease, hypertension, and renal disease were the top five pre-existing conditions that altogether predicted 29% of outcomes. 79% of the cases with poor prognosis were predicted based on the combination of variables. Age stratified models revealed that among younger adults (18–40 Y), obesity was among the top risk factors associated with adverse outcomes.ConclusionDecision tree modeling has identified key factors associated with a significant proportion of severe outcomes in COVID-19. Knowledge about these variables will aid in identifying high-risk groups and allocating health care resources.

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Section: Discussionsupporting

confidence: 91%

Predicting the Risk Factors Associated With Severe Outcomes Among COVID-19 Patients–Decision Tree Modeling Approach

Kumaran

Pham

Wang

et al. 2022

Front. Public Health

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“…Interestingly, the lethality of COVID-19 in the city of Rio de Janeiro is significantly higher in the Metropolitana region than in others (Fig. S9), a result previously reported [40,41]. These studies propose that the high social and economic inequality within the city of Rio de Janeiro in association with better diagnosis and notification systems resulted in this discrepancy when compared to other regions or cities.…”

Section: Resultssupporting

confidence: 63%

Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants

et al. 2022

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During the first semester of 2021, all of Brazil has suffered an intense wave of COVID-19 associated with the Gamma variant. In July, the first cases of Delta variant were detected in the state of Rio de Janeiro. In this work, we have employed phylodynamic methods to analyse more than 1 600 genomic sequences of Delta variant collected until September in Rio de Janeiro to reconstruct how this variant has surpassed Gamma and dispersed throughout the state. After the introduction of Delta, it has initially spread mostly in the homonymous city of Rio de Janeiro, the most populous of the state. In a second stage, dispersal occurred to mid- and long-range cities, which acted as new close-range hubs for spread. We observed that the substitution of Gamma by Delta was possibly caused by its higher viral load, a proxy for transmissibility. This variant turnover prompted a new surge in cases, but with lower lethality than was observed during the peak caused by Gamma. We reason that high vaccination rates in the state of Rio de Janeiro were possibly what prevented a higher number of deaths.

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“…Figura 3 -Descrição dos artigos, segundo autor, ano, local, tipo de estudo, amostra e principais resultados. São Carlos, SP, Brasil, 2023 Dos 15 estudos analisados, no que se refere às desvantagens socioeconômicas, três evidenciaram que as relações sociais e econômicas afetaram a incidência e mortalidade de crianças pelo vírus (7,13,21) , oito demonstraram vulnerabilidade social com destaque para baixa renda e pobreza (7,(11)(12)(13)(20)(21)(22)24) , seis salientaram os fatores étnicos/raciais (15)(16)(17)(18)(19)(20) , e três, as desigualdades econômicas (7,(20)(21) , que exerceram influência na mortalidade. Além disso, também indicaram variáveis relacionadas ao tamanho da família e moradia lotada (24) , família monoparental (21) , país de origem e problemas graves de moradia (21) , condições sanitárias (11) e geográficas (20) , e regiões geopoliticamente desfavorecidas (15,23) .…”

Section: Resultsunclassified

Influence of socioeconomic factors on COVID-19 mortality in children: a scoping review

Silva,

Ribeiro,

Uehara

2023

Rev Rene

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Objective: to identify the scientific evidence on the relationship between socioeconomic factors and mortality in children due to COVID-19. Methods: this is a scoping review. Searches were carried out in the following databases: US National Library of Medicine - National Institutes of Health; Scientific Electronic Library Online; Institute for Scientific Information; Cumulative Index to Nursing and Allied Health Literature; and SciVerse Scopus. Results: 15 articles were selected that showed the influence of socioeconomic factors on infant mortality from Severe Acute Respiratory Syndrome Coronavirus 2. Conclusion: it was shown that socioeconomic factors are related to increased mortality from COVID-19 in the infant population, low income and residence in locations considered to be of greater socioeconomic vulnerability, which were presented as important variables to be considered in the COVID-19 pandemic. Contributions to practice: several factors are related to increased susceptibility to Severe Acute Respiratory Syndrome Coronavirus infection and worsening of the disease in children. However, understanding that socioeconomic factors can be a determinant of COVID-19 incidence and mortality in children highlights the need to invest in actions aimed at reducing socioeconomic inequalities, to reduce preventable deaths.

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Correction to: Factors associated with death in confirmed cases of COVID-19 in the state of Rio de Janeiro

Cited by 6 publications

References 1 publication

Predicting the Risk Factors Associated With Severe Outcomes Among COVID-19 Patients–Decision Tree Modeling Approach

Predicting the Risk Factors Associated With Severe Outcomes Among COVID-19 Patients–Decision Tree Modeling Approach

Phylodynamic analysis of SARS-CoV-2 spread in Rio de Janeiro, Brazil, highlights how metropolitan areas act as dispersal hubs for new variants

Influence of socioeconomic factors on COVID-19 mortality in children: a scoping review

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